Matching Mobile Applications for Cross-Promotion

Published Online:https://doi.org/10.1287/isre.2020.0921

References

  • Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge Data Engrg. 17(6):734–749.CrossrefGoogle Scholar
  • Adomavicius G, Bockstedt J, Curley SP (2015) Bundling effects on variety seeking for digital information goods. J. Management Inform. Systems 31(4):182–212.CrossrefGoogle Scholar
  • Anderson C (2006) The Long Tail: Why the Future of Business is Selling Less of More (Hachette Books, New York).Google Scholar
  • Askalidis G (2016) The impact of large scale promotions on the sales and ratings of mobile apps: Evidence from Apple’s app store. Working paper, Northwestern University, Evanston, IL.Google Scholar
  • Bart Y, Stephen AT, Sarvary M (2014) Which products are best suited to mobile advertising? A field study of mobile display advertising effects on consumer attitudes and intentions. J. Marketing Res. 51(3):270–285.CrossrefGoogle Scholar
  • Bhargava HK (2012) Retailer-driven product bundling in a distribution channel. Marketing Sci. 31(6):1014–1021.LinkGoogle Scholar
  • Blei DM (2012) Probabilistic topic models. Comm. ACM 55(4):77–84.CrossrefGoogle Scholar
  • Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J. Machine Learn. Res. 3(1):993–1022.Google Scholar
  • Bresnahan T, Greenstein S (2014) Mobile computing: The next platform rivalry. Amer. Econom. Rev. 104(5):475–480.CrossrefGoogle Scholar
  • Brynjolfsson E, Hu Y(J), Smith MD (2010) Long tails vs. superstars: The effect of information technology on product variety and sales concentration patterns. Inform. Systems Res. 21(4):736–747.LinkGoogle Scholar
  • Burke R (2002) Hybrid recommender systems: Survey and experiments. User Model. User-Adapted Interaction 12(4):331–370.CrossrefGoogle Scholar
  • Carare O (2012) The impact of bestseller rank on demand: Evidence from the app market. Internat. Econom. Rev. 53(3):717–742.CrossrefGoogle Scholar
  • Davis J, Goadrich M (2006) The relationship between precision-recall and roc curves. Proc. 23rd Internat. Conf. Machine Learn. (Association for Computing Machinery, New York), 233–240.Google Scholar
  • Eliaz K, Spiegler R (2016) Search design and broad matching. Amer. Econom. Rev. 106(3):563–586.CrossrefGoogle Scholar
  • Ershov D (2016) The effect of consumer search costs on entry and quality in the mobile app market. Working paper, University of Toronto, Toronto.Google Scholar
  • Evans DS, Schmalensee R, Noel MD, Chang HH, Garcia-Swartz DD (2011) Platform Economics: Essays on Multi-Sided Businesses (Competition Policy International, Boston).Google Scholar
  • Fong NM (2017) How targeting affects customer search: A field experiment. Management Sci. 63(7):2353–2364.LinkGoogle Scholar
  • Gale D, Shapley LS (1962) College admissions and the stability of marriage. Amer. Math. Monthly 69(1):9–15.CrossrefGoogle Scholar
  • Garg R, Telang R (2013) Inferring app demand from publicly available data. MIS Quart. 37(4):1253–1264.CrossrefGoogle Scholar
  • Ghose A, Han SP (2014) Estimating demand for mobile applications in the new economy. Management Sci. 60(6):1470–1488.LinkGoogle Scholar
  • Ghose A, Goldfarb A, Han SP (2012) How is the mobile Internet different? Search costs and local activities. Inform. Systems Res. 24(3):613–631.LinkGoogle Scholar
  • Ghose A, Li B, Liu S (2019) Mobile targeting using customer trajectory patterns. Management Sci. 65(11):5027–5049.LinkGoogle Scholar
  • Griffiths TL, Steyvers M (2004) Finding scientific topics. Proc. Natl. Acad. Sci. USA 101(Suppl. 1):5228–5235.CrossrefGoogle Scholar
  • Guo, Hong, Xuying Zhao, Lin Hao, De Liu (2019) Economic analysis of reward advertising. Production Oper. Management 28(10):2413–2430.CrossrefGoogle Scholar
  • Hagiu A (2006) Pricing and commitment by two-sided platforms. RAND J. Econom. 37(3):720–737.CrossrefGoogle Scholar
  • Han SP, Park S, Oh W (2016) Mobile app analytics: A multiple discrete-continuous choice framework. MIS Quart. 40(4):983–1008.CrossrefGoogle Scholar
  • Hariri N, Mobasher B, Burke R (2012) Context-aware music recommendation based on latent topic sequential patterns. Proc. ACM Conf. Recommender Systems (Association for Computing Machinery, New York), 131–138.Google Scholar
  • Hatfield JW, Kominers SD (2017) Contract design and stability in many-to-many matching. Games Econom. Behav. 101:78–97.CrossrefGoogle Scholar
  • Heckman JJ (1979) Sample selection bias as a specification error. Econometrica 47(1):153–161.CrossrefGoogle Scholar
  • Hu W, Bolivar A (2008) Online auctions efficiency: A survey of eBay auctions. Proc. Internat. Conf. World Wide Web (Association for Computing Machinery, New York), 925–934.Google Scholar
  • Ifrach B, Johari R (2014) The impact of visibility on demand in the market for mobile apps. Preprint, submitted June 24, https://ssrn.com/abstract=2444542.Google Scholar
  • Johnson MD, Herrmann A, Huber F (2006) The evolution of loyalty intentions. J. Marketing 70(2):122–132.CrossrefGoogle Scholar
  • Kamakura WA (2008) Cross-selling: Offering the right product to the right customer at the right time. J. Relationship Marketing 6(3–4):41–58.CrossrefGoogle Scholar
  • Kamakura WA, Kossar BS, Wedel M (2004) Identifying innovators for the cross-selling of new products. Management Sci. 50(8):1120–1133.LinkGoogle Scholar
  • Le Q, Mikolov T (2014) Distributed representations of sentences and documents. Xing EP, Jebara T, eds. Proc. Internat. Conf. Machine Learn., Proc. Machine Learn. Res. 32(2):1188–1196.Google Scholar
  • Lee GM, Qiu L, Whinston AB (2016) A friend like me: Modeling network formation in a location-based social network. J. Management Inform. Systems 33(4):1008–1033.CrossrefGoogle Scholar
  • Lee, G, Raghu TS (2014) Determinants of mobile apps’ success: Evidence from the app store market. J. Management Inform. Systems 31(2):133–170.CrossrefGoogle Scholar
  • Lee Y, O’Connor GC (2003) New product launch strategy for network effects products. J. Acad. Marketing Sci. 31(3):241–255.CrossrefGoogle Scholar
  • Levin JD (2011) The economics of internet markets. NBER Working Paper No. 16852, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Li MM, Huang Y, Sinha A (2017) Data-driven promotion planning for mobile applications. Working paper, University of Michigan, Ann Arbor.Google Scholar
  • Li S, Sun B, Montgomery AL (2011) Cross-selling the right product to the right customer at the right time. J. Marketing Res. 48(4):683–700.CrossrefGoogle Scholar
  • Li S, Sun B, Wilcox RT (2005) Cross-selling sequentially ordered products: An application to consumer banking services. J. Marketing Res. 42(2):233–239.CrossrefGoogle Scholar
  • Li X, Bresnahan T, Yin P-L (2016) Paying incumbents and customers to enter an industry: Buying downloads. Working paper, Stanford University, Stanford, CA.Google Scholar
  • Linden G, Smith B, York J (2003) Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 7(1):76–80.CrossrefGoogle Scholar
  • Lops P, De Gemmis M, Semeraro G (2011) Content-based recommender systems: State of the art and trends. Recommender Systems Handbook (Springer, Boston), 73–105.Google Scholar
  • Luo X, Zhang J, Duan W (2013) Social media and firm equity value. Inform. Systems Res. 24(1):146–163.LinkGoogle Scholar
  • Marotta V, Zhang K, Acquisti A (2018) The welfare impact of targeted advertising. Working paper, Carnegie Mellon University, Pittsburgh, PA.Google Scholar
  • McAlister L (1982) A dynamic attribute satiation model of variety-seeking behavior. J. Consumer Res. 9(2):141–150.CrossrefGoogle Scholar
  • Molitor D, Reichhart P, Spann M, Ghose A (2015) Measuring the effectiveness of location-based pull advertising: A randomized field experiment. Working paper, Fordham University, New York.Google Scholar
  • Nalebuff B (2004) Bundling as an entry barrier. Quart. J. Econom. 119(1):159–187.CrossrefGoogle Scholar
  • Natarajan N, Shin D, Dhillon IS (2013) Which app will you use next? Collaborative filtering with interactional context. Proc. ACM Conf. Recommender Systems (Association for Computing Machinery, New York), 201–208.Google Scholar
  • Oestreicher-Singer G, Sundararajan A (2012) Recommendation networks and the long tail of electronic commerce. MIS Quart. 36(1):65–84.CrossrefGoogle Scholar
  • Oliver RL (1999) Whence consumer loyalty? J. Marketing 63(4):33–44.CrossrefGoogle Scholar
  • Petsas T, Papadogiannakis A, Polychronakis M, Markatos EP, Karagiannis T (2013) Rise of the planet of the apps: A systematic study of the mobile app ecosystem. Proc. ACM Internet Measurement Conf. (Association for Computing Machinery, New York), 277–290.Google Scholar
  • Qiu Y, Gopal A, Hann I-H (2017) Logic pluralism in mobile platform ecosystems: A study of indie app developers on the iOS app store. Inform. Systems Res. 28(2):225–249.LinkGoogle Scholar
  • Rafieian O, Yoganarasimhan H (2018a) How does variety of previous ads influence consumer’s ad response? Working paper, University of Washington, Seattle.Google Scholar
  • Rafieian O, Yoganarasimhan H (2018b) Targeting and privacy in mobile advertising. Working paper, University of Washington, Seattle.Google Scholar
  • Ramage D, Dumais ST, Liebling DJ (2010) Characterizing microblogs with topic models. Proc. Internat. AAAI Conf. Web Social Media (AAAI Press, Palo Alto, CA), 130–137.Google Scholar
  • Read D, Loewenstein G (1995) Diversification bias: Explaining the discrepancy in variety seeking between combined and separated choices. J. Experiment. Psych. Appl. 1(1):34–49.CrossrefGoogle Scholar
  • Resnick P, Varian HR (1997) Recommender systems. Comm. ACM 40(3):56–58.CrossrefGoogle Scholar
  • Rochet J-C, Tirole J (2003) Platform competition in two-sided markets. J. Eur. Econom. Assoc. 1(4):990–1029.CrossrefGoogle Scholar
  • Roth AE (1984) The evolution of the labor market for medical interns and residents: A case study in game theory. J. Political Econom. 92(6):991–1016.CrossrefGoogle Scholar
  • Roth AE (2008) What have we learned from market design? Econom. J. 118(527):285–310.CrossrefGoogle Scholar
  • Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inform. Processing Management 24(5):513–523.CrossrefGoogle Scholar
  • Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. Brusilovsky P, Kobsa A, Nejdl W, eds. The Adaptive Web: Methods and Strategies of Web Personalization (Springer, Berlin), 291–324.Google Scholar
  • Shi Z, Lee GM, Whinston AB (2016) Toward a better measure of business proximity: Topic modeling for industry intelligence. MIS Quart. 40(4):1035–1056.CrossrefGoogle Scholar
  • Shin D, He S, Lee GM, Whinston AB, Cetintas S, Lee K-C (2020) Enhancing social media analysis with visual data analytics: A deep learning approach. MIS Quart. Forthcoming.Google Scholar
  • Simonson I (1990) The effect of purchase quantity and timing on variety-seeking behavior. J. Marketing Res. 27(2):150–162.CrossrefGoogle Scholar
  • Simonson I, Winer RS (1992) The influence of purchase quantity and display format on consumer preference for variety. J. Consumer Res. 19(1):133–138.CrossrefGoogle Scholar
  • Smith MD, Telang R (2009) Competing with free: The impact of movie broadcasts on DVD sales and Internet piracy. MIS Quart. 33(2):321–338.CrossrefGoogle Scholar
  • Soman D, Gourville JT (2001) Transaction decoupling: How price bundling affects the decision to consume. J. Marketing Res. 38(1):30–44.CrossrefGoogle Scholar
  • Sørensen M (2007) How smart is smart money? A two-sided matching model of venture capital. J. Finance 62(6):2725–2762.CrossrefGoogle Scholar
  • Stremersch S, Tellis GJ (2002) Strategic bundling of products and prices: A new synthesis for marketing. J. Marketing 66(1):55–72.CrossrefGoogle Scholar
  • Venkatesh R, Mahajan V (2009) The design and pricing of bundles: A review of normative guidelines and practical approaches. Handbook of Pricing Research in Marketing (Edward Elgar, Cheltenham, UK), 232–257.Google Scholar
  • Wang C, Blei DM (2011) Collaborative topic modeling for recommending scientific articles. Proc. ACM Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 448–456.Google Scholar
  • Weng J, Lim E-P, Jiang J, He Q (2010) TwitterRank: Finding topic-sensitive influential Twitterers. Proc. ACM Internat. Conf. Web Search Data Mining (Association for Computing Machinery, New York), 261–270.Google Scholar
  • Xu L, Duan JA, Whinston AB (2014) Path to purchase: A mutually exciting point process model for online advertising and conversion. Management Sci. 60(6):1392–1412.LinkGoogle Scholar
  • Yin P-L, Davis JP, Muzyrya Y (2014) Entrepreneurial innovation: Killer apps in the iPhone ecosystem. Amer. Econom. Rev. 104(5):255–259.CrossrefGoogle Scholar
  • Zhong N, Michahelles F (2013) Google Play is not a long tail market: An empirical analysis of app adoption on the Google Play app market. Proc. 28th Annual ACM Sympos. Applied Comput. (Association for Computing Machinery, New York), 499–504.Google Scholar
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